Python Numpy Array Shared Between Processes
Python’s multiprocessing.shared_memory module provides a way to share a block of memory between processes. It’s usefull in ml contexts, where an app will runs ML model in separate process and wants share the results with its parent process. Frigate NVR does exacly that, see: object_detection.py.
# Define properties of shared memory
SHARED_MEMORY_NAME = "shm-1"
SHARED_MEMORY_SHAPE = (5, 5)
NP_DATA_TYPE = np.float64
# Calculate shared numpy array size
dsize = np.dtype(NP_DATA_TYPE).itemsize * np.prod(SHARED_MEMORY_SHAPE).astype(int)
# Create allocate shared memory with a given name
shm = shared_memory.SharedMemory(
create=True,
size=dsize,
name=SHARED_MEMORY_NAME,
)
To access it from a different process, create SharedMemory with create=False and the same name.
For example:
class ShmModifier(Process):
def __init__(self) -> None:
# Required as per: https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process
Process.__init__(self)
# Get shared memory
self.out_shm = shared_memory.SharedMemory(name=SHARED_MEMORY_NAME, create=False)
# Map it to numpy array
self.out_np = np.ndarray(
SHARED_MEMORY_SHAPE, dtype=np.float32, buffer=self.out_shm.buf
)
def run(self) -> None:
# Overrides a numpy array (runs in a different process)
self.out_np[0, 0] = 1
self.out_shm.close()
Full working example
import numpy as np
from multiprocessing import Process
import multiprocessing.shared_memory as shared_memory
SHARED_MEMORY_NAME = "shm-1"
SHARED_MEMORY_SHAPE = (5, 5)
NP_DATA_TYPE = np.float64
class ShmModifier(Process):
def __init__(self) -> None:
# Required as per: https://docs.python.org/3/library/multiprocessing.html#multiprocessing.Process
Process.__init__(self)
# Get shared memory
self.out_shm = shared_memory.SharedMemory(name=SHARED_MEMORY_NAME, create=False)
# Map it to numpy array
self.out_np = np.ndarray(
SHARED_MEMORY_SHAPE, dtype=np.float32, buffer=self.out_shm.buf
)
def run(self) -> None:
# Overrides a numpy array (runs in a different process)
self.out_np[0, 0] = 1
self.out_shm.close()
if __name__ == "__main__":
# Calculate shared numpy array size
dsize = np.dtype(NP_DATA_TYPE).itemsize * np.prod(SHARED_MEMORY_SHAPE).astype(int)
# Create allocate shared memory with a given name
shm = shared_memory.SharedMemory(
create=True,
size=dsize,
name=SHARED_MEMORY_NAME,
)
out_np = np.ndarray(SHARED_MEMORY_SHAPE, dtype=np.float32, buffer=shm.buf)
# Fill arr with zeros
out_np[:] = np.zeros_like(out_np)
print("Array before running the process:")
print(out_np)
dp = ShmModifier()
dp.start()
dp.join()
print("Array after running the process:")
print(out_np)
shm.unlink()
Running this script will result in:
Array before running the process:
[[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]
Array after running the process:
[[1. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]]